GNSS Observation Data Research Articles

Tropospheric delay estimation and influencing factors analysis of GNSS multi-system

Abstract In this paper, the tropospheric delay parameters are estimated by GNSS regional network solution, and the different factors affecting the estimation of tropospheric delay parameters are analyzed experimentally. The high-precision tropospheric delay correction file (.ztd) provided by IGS and the traditional model estimation of the GNSS regional network are compared to verify the final positioning accuracy. The main factors affecting the estimation of tropospheric delay parameters and the conditions conducive to the estimation of tropospheric delay parameters in the data solution process are obtained. The experimental results show that: (1) GPS has the highest tropospheric delay estimation accuracy in the European region, Galileo is slightly worse than GPS, and BDS has the lowest tropospheric delay estimation accuracy; (2) The tropospheric delay estimation accuracy of post-product in different satellite precision products is better than that of real-time products, and CLKM2 is better than CLK93 in real-time products. With the increase of satellite systems, the accuracy of tropospheric delay estimation is gradually improved. Compared with the GPS single system, the accuracy of the GPS+ Galileo+BDS triple system is improved by about 7.2%, 7.2%, and 7.5%; (3) The tropospheric delay estimation accuracy of the European region is better than that of the Australian region; (4) Based on the BDS whole system data solution, the high-precision tropospheric delay correction file (.ztd ) has better positioning accuracy, and the tropospheric delay estimated by the GNSS observation data through the traditional Saastamoninen model of the regional network can also meet the current high-precision positioning requirements in terms of positioning accuracy. The horizontal direction is better than 2 cm, and the elevation direction is better than 4 cm.

Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms

Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms

Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence.

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.

Analysis of 5G Smart Communication Base Station Doppler-Smoothed Pseudorange Single-Point Geodesic Positioning Accuracy

With the continuous development of mobile communication and satellite navigation technologies, the positioning requirements of 5G smart communication base stations are becoming higher and higher. With the opening of GNSS raw observation data, research on the positioning of a 5G smart communication base station has become a research hotspot in the surveying and mapping disciplines. In this paper, based on the GNSS observation data of the 5G smart communication base station, the quality of the original GNSS observation data of the 5G smart communication base station is studied and analyzed. A method based on Doppler smoothing pseudorange solves the problem that the original pseudorange observation values of the 5G smart communication base station are noisy and prone to multipath errors due to the limitations of the base station chips and processes, which makes the traditional data processing methods unable to meet the demand for higher accuracy positioning. This method uses Doppler observations to smooth the pseudorange and determines the Doppler smoothing strategy and closure values to improve the data quality. The experimental data show that Doppler smoothing pseudorange can improve data quality and positioning accuracy by 67.9% in the E direction, 64.8% in the N direction, and 65.5% in the U direction. The future world will develop in the direction of intelligence, and the wireless network 5G technology used to support the construction of this intelligent system will become the core driver for the development of a leading intelligent society. 5G network signals have higher reliability and lower latency and can meet the specific needs of smart manufacturing, autonomous driving, and other industrial applications. This new base station product can meet the construction needs of future 5G base stations, adapt to the future intensive, miniaturized, intelligent station construction mode, and realize safe and fast station construction, providing the necessary hardware support for 5G network coverage.

Парамуширское землетрясение 25 марта 2020 г. Mw = 7.4

The strong Mw = 7.4 earthquake occurred on March 25, 2020 in the region of Northern Kuril Islands, with its epicenter on the ocean side of the Kuril-Kamchatka deep-water trench, to the east from its axis. The earthquake was felt on all the Kuril Islands, South and East Kamchatka, the maximum shaking was recorded in Severo-Kurilsk with intensity I = VI–VII. Distinct tsunami wave was also registered. In the article, this earthquake and its tectonic position are discussed in the context of the seismicity of the Kuril-Kamchatka arc. The actions of duty shifts in earthquake processing are described, and a detailed description of macroseismic effects is given. The results of the analysis of peak ground motion amplitudes, focal mechanisms and models of the source, tsunami propagation features are also shown. Coseismic displacements revealed by GNSS observation data are presented and compared with model data. Peculiarities and stages of the aftershock process are discussed and the size of the source area is estimated.

Design of Ionospheric TEC Gradient Anomaly Detection Platform Based on GNSS

Abstract Ionospheric anomalies can seriously threaten important flight phases such as approach and landing, so accurate real-time monitoring is necessary. In this paper, an ionospheric anomaly gradient detection platform is designed for satellite navigation receiver. Based on GNSS observation data, the spatial gradient of ionospheric vertical total electron content is calculated, and anomaly ionospheric gradient is detected using generalized likelihood ratio test method. To verify the correctness of detection results, rate of TEC change index is also calculated for comparison. Simulation results show that the platform can detect ionospheric anomaly gradient state fast and accurately, and the proposed approach can used by GNSS receivers to detect ionospheric anomaly.

Displacement Velocity and Strain Analysis of Opak Fault Monitoring Stations

The Opak Fault is an active fault that can potentially cause earthquakes in Yogyakarta. Periodic monitoring of the Opak Fault activity was previously used more GNSS observation data from the measurement campaign by the Geodesi Geometri dan Geodesi Fisis (GGGF) Laboratory Team, Geodetic Engineering Department, Faculty of Engineering, Universitas Gadjah Mada. However, there are several CORS BIG stations located in Yogyakarta. The CORS BIG data is used to increase the precision of the Opak Fault monitoring station. Therefore, the addition of the CORS is evaluated to obtain a displacement in the monitoring station. The computation of the displacement velocity value of the Opak Fault monitoring station has been done before using the Linear Least Square Collocation and grid search methods. The other method, namely the kriging method, needs to be evaluated for producing a more precise displacement velocity value. The research data includes GNSS campaign and CORS BIG data for six years, 2013 to 2020. The CORS stations around DIY are JOGS and CBTL. The GNNS data were processed to determine the solution for the daily coordinate, displacement, and standard deviation values for each Opak Fault monitoring station. The displacement velocity value is generated by the Linear Least Square method then reduced from the influence of the Sunda Block. The velocity value is used in the strain value estimation around the Opak Fault area at each station using the kriging method combined with the gaussian sequential simulation technique. The estimated displacement velocities are examined for statistical significance compared to the research of Adam (2019) and Pinasti (2019). This research generates the value of the displacement velocity in the east and north components of 12.39 to 30.99 mm/year and 1.96 to -14.11 mm/year, respectively. The displacement direction of all monitoring stations is dominant to the southeast. The Sunda Block reduced the displacement velocity. The east and north components are -2.32 to 2.28 mm/year and -0.52 to 4.2 mm/year, respectively. The displacement direction is towards the northwest. The strain estimation using the kriging method combined with the gaussian sequential simulation technique obtained an average strain value of 0.05 microstrain/year. The result of the data processing at each station has different arrow lengths, meaning that each location has a different strain value.

High-Accuracy GNSS Positioning of Ocean Vessels for Sea Wave Monitoring

Kim, M. and Bae, T.-S., 2021. High-accuracy GNSS positioning of ocean vessels for sea wave monitoring. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 415–419. Coconut Creek (Florida), ISSN 0749-0208. This study aims to develop a high-precision strategy for the positioning of marine vessels to monitor sea waves. Thus, the total electron content (TEC) in the ionosphere will be accurately corrected in the positioning process. We analyzed various aspects of ionospheric delay using the Global Ionosphere Map (GIM). The results indicated that the ionosphere was affected by the frequencies and long-term changes of the TEC. It is critical to synchronize the correction information and GNSS observation data on ocean vessels. We developed a methodology for the time synchronization of the GIM, specifically for the ocean, on which only a small number of continuously operating reference station (CORS) networks are available. The generated ionosphere model was compared to the GIM with a maximum of 1.6 TEC, which should be verified for a longer period of time.

High Precision Algorithm for Ionospheric VTEC Based on Single Ground-based GNSS Station

Using the dual frequency GNSS observation data provided by IGS, the problems existing in Vertical Total Electron Content (VTEC) calculation using Kalman filtering method were analyzed, Kriging-Kalman algorithm was proposed, and the VTEC calculated by the two methods were compared. The results showed that: in the low latitude region, when the number of satellites in observation changed, the VTEC value calculated by Kalman filter method might have curve fracture anomaly; the VTEC value calculated by Kriging-Kalman method changed smoothly relatively. In addition, the changes of VTEC calculated by two method above during the flare were compared, it found that the change of VTEC calculated by Kalman filtering method was smaller than the increment of VTEC caused by flare, while the result of Kriging-Kalman method was more consistent with the actual change. All the results show that the accuracy of VTEC calculated using Kriging-Kalman method is higher in low latitude area, which can reflect the change of VTEC in abnormal space weather activities more accurately, and conducive to the daily accurate monitoring and engineering application of ionospheric VTEC.

Analysis of accuracy the InaCORS BIG online post-processing service

Applications for control point topographic mapping are currently almost done by the GNSS observation method. One method provided by the Indonesia Geospatial Information Agency (BIG) is an online post-processing called InaCORS. This process is achieved by uploading GNSS observation data (RINEX) to the web http://nrtk.big.go.id/ . With this method, using only 1 GNSS receiver, control points for mapping can be obtained, and the calculation processing does not require in-depth knowledge of baseline processing. The purpose of this research was to determine the accuracy of the online post-processing InaCORS method if it is used as a mapping control point with a baseline between 500 and 3500 m. The biggest difference in the results are at northing (N) = 0.028 m easting (E) = 0.060 m, and elevation (z) = 0.084 m.

GDP: an open-source GNSS data preprocessing toolkit

GNSS Data Preprocessor is a multi-GNSS data preprocessing software designed to process raw GNSS observation data in the Receiver Independent Exchange Format 2.x to 3.x standard. Published under a free and open-source license, LGPL (GNU Lesser General Public License), written in object-oriented programming language C#, it mainly includes multi-GNSS file and IGS product automatic acquisition, format conversion, file selection, data visualization, and analysis of GNSS observation files. It provides both a Windows form interface and a command shell interface for the Windows, Linux, or macOS operating system. Volunteers can also participate in and improve the software through GitHub. This software is continuing to evolve, improving its functionalities according to the updates introduced by the collaborators. We give a brief introduction to the software, including software architecture, functions, and modules. Lastly, we give several common examples, including parallel computing, visual satellite graphical display, and cycle slip detection, to show the working status of the current version of the software.

Positioning bias of different frequency observations in double-differenced relative positioning

In this study, the double-differenced positioning bias of different frequency observations (PBDFO) in a code division multiple access (CDMA) signal system is analyzed based on real-time kinematic (RTK) positioning using raw observations at different frequencies. To test and verify the existence of the DD PBDFO, a set of ultra-short baseline (25 m) GNSS observation data were collected. The stations NT03 and NT04 were located in Xi’an City, Shaanxi Province. The observation time ranged from UTC 11:00 to 17:00 for 10 consecutive days from DOY 012 to DOY 021 in 2017. The results showed that there are mm–cm differences in the RTK solution results for the raw observations at different frequencies, the mean bias and the standard deviation (STD) of the positioning results of GPS L1 and L2 in the East, North, and Up components are (−0.595 mm, 1.35 mm, −0.133 mm) and (3.11 mm, 3.32 mm, 6.44 mm), respectively, but for BDS B1 and B2, the corresponding values are (2.16 mm, −4.24 mm, 9.48 mm) and (3.35 mm, 4.85 mm, 7.41 mm), respectively. The PBDFO between GPS L1 and L2 frequencies is lower in magnitude and more stable than the PBDFO between BDS B1 and B2 frequencies, the mean and the STD of the PBDFO between GPS L1 and L2 obtained from the parameter estimation method in the East, North, and Up components are (−0.606 mm, 1.03 mm, −0.217 mm) and (0.162 mm, 0.169 mm, 0.276 mm), respectively; but for BDS B1 and B2, the corresponding values are (3.00 mm, −3.83 mm, 8.59 mm) and (0.375 mm, 1.14 mm, 2.47 mm), respectively. Both PBDFO correction model and parameter estimation approach can achieve consistency in terms of the RTK positioning results at different frequencies. The double-differenced (DD) PBDFO obtained from the direct difference method and parameter estimation approach are consistent and relatively stable for the same period of continuous days.

Analysis of the Effect of Different Combinations of Observation Satellites on the Resolving Accuracy of GNSS Observation Data

In this paper, 24 C-level control points under different terrain conditions were selected to be the testing points. The binary-satellite system (GPS+GLONASS) and the triple-satellite system with BeiDou Navigation Satellite System (BDS) (BDS+GPS+GLONASS) were adopted for static measurement; and the observation data from BeiDou Ground-based Augumentation System (GBAS) base stations in Guangxi were collected for solution. By comparing the residuals of GPS tri-dimensional baseline vectors and the internal accord accuracy of each control point under the binary and triple-satellite systems, the effect of data collected by different satellite systems under different terrain conditions on measurement accuracy was studied. According to the results, (1) the triple-satellite system with BDS showed more stable measurement accuracy; (2) in plane, the two systems were of equivalent measurement accuracy in mountainous and flat areas; in elevation, the triple-satellite system showed higher and more stable measurement accuracy.

The effect of weights observation to estimate the monitoring control point coordinate at sermo dam

The use of weights observation was effected on the estimation of coordinates and its precision. This research aims to identify 3D coordinates and its precision using weights observation from the result of GAMIT processing and the law of error propagation in sequential adjustment method. This research uses GNSS observation data from 10 deformation monitoring control points around Sermo Dam that was measured on doy 250 in 2014 and doy 129 in 2015. Data processing was performed by GAMIT software to get a baseline length and its precision. Baseline length and its precision were used as input in the sequential adjustment method. The values of weight observation were calculated using the result of GAMIT processing and the law of error propagation. The result of this research are 3D coordinates control point and its precision. The coordinate results using weight observation from GAMIT result does not significantly different from the law of random error propagation result, whereas the precision result is different significantly. The differences of precision reached 2 centimetres.

Utilization of Tide Observation and Satellite Altimetry Data for Detecting Land Vertical Movement

Indonesia is one of the country that often experiences earthquakes and volcanic eruptions because of its position on the meeting point of three active tectonic plates, namely the Eurasian, Indo-Australian and Pacific plates. This causes movement or deformation both horizontally and / or vertically. Current movement monitoring is done using GNSS technology. However, the distribution of monitoring points are not spread evenly and the results of GNSS observation have weaknesses in their vertical components. An alternative method for monitoring movement especially in vertical components is using combination of tide observation and satellite altimetry data. The measurement concept of satellite altimetry produces absolute sea level values and its not affected by plate movements,besides tidal measurements which sensors are installed on the ground so that the sea level values are affected by land uplift or subsidence at that location. This study used main data, namely long-period tidal observation data and GDR (Geophysical Data Record) satellite altimetry. GNSS observation data is needed as a control of vertical movement. The differences in spatial and temporal resolution of tidal observation and satellite altimetry are overcome by two methods, namely the daily and monthly methods. Each of these methods will produce an annual SLR value (Sea Level Rise) from tidal data and satellite altimetry calculated using linear regression. The difference between the tidal and satellite altimetry data trends is compared with the vertical movement value from GNSS data. The results of the study indicate that the determination of vertical movement can be done using tidal observation and satellite altimetry data, especially using the monthly method. The trend of vertical movements of tidal observation and satellite altimetry data shows the same pattern with land vertical movement of GNSS data even though the value is different. Therefore, the method to determine vertical movement from hydrological data can be used as an initial observation on a coastal area regarding the phenomenon of vertical movements that occur in the area.

Correlation of GNSS Observation Data Quality Resulted from TEQC Checking and Coordinate’s Precision

GPS Positioning provides good coordinate accuracy that is up to a millimeter. However, some error sources such as multipath, atmospheric conditions and obstruction can reduce the quality of data and also coordinates. To minimize errors due to these factors, at the time of determining the station location, it is necessary to pay attention to the surrounding conditions, namely by looking for open areas and avoiding objects that can reflect GNSS signals. However, it is often not easy to find the ideal observation station location, which forms a good chain while being free from obstruction and multipath. Therefore, it is often necessary to prioritize certain factors over other factors. Information about the correlation between multipath, ionospheric conditions and the recording level of observational data on coordinate accuracy can be used as consideration in determining the location of control points for deformation monitoring and determining which factors are prioritized. This study aims to evaluate the correlation between data quality and coordinates precision.The used observation data are Sermo Reservoir control network and nine CORS BIG stations. The component data analyzed are multipath (MP1, MP2), ionospheric effects (IOD slips and IOD or MP slips) and the data recording level (obs). These components were resulted by checking with TEQC software, while the precision of the coordinates was obtained by processing with GAMIT / GLOBK software. Based on the correlation coefficient value, it is known that the recording level of observation data has the strongest correlation with a negative direction (ranging from -0.7 to -0.9). It is the ratio between the number of real observations to the number of possible ones. One factor that influences it is the obstruction in the field. In other words, in determining the location of GNSS observation stations, the conditions of obstruction in the vicinity need to be considered and prioritized.

PENGGUNAAN KOMBINASI TITIK IKAT STASIUN GLOBAL IGS DAN TITIK IKAT STASIUN GPS REGIONAL YANG DIIKATKAN PADA ITRF-2008 DALAM PENDEFINISIAN STASIUN GNSS CORS GMU1

The CORS GNSS station can be used as a reference point in determining relative positions in both real time and post processing. This reference point is used for the measurement of points on the surface of the earth. In this measurement requires a high accuracy that is up to a fraction of a millimeter. In this research, GNSS CORS station which is used is GNSS CORS GMU1 which has been operated since 2009 which is located in Engineering Faculty Building of Department of Geodesy Engineering (Level Three) Universitas Gadjad Mada Yogyakarta, Indonesia. This study redefined the coordinates of GNSS CORS GMU1 using GNSS observation data for seven days from July 14, 2012 until July 20, 2012 using a combination of IGS and regional global bundle points. The tipping points of IGS global station used in this research are DGAR, GUAM, IISC, KARR, KUNM, PIMO, and TOW2. While the binding point of Regional station used is BAKO, CJPR, CDNP, CSRJ, SAMP, CBAL and CBIT. Data processing using scientific software GAMIT / GLOBK. The result of this research is 3D cartesian coordinate value from GNSS CORS GMU1 station in 2012 and its position speed. Coordinate of processing result that is X (m) = -2200206,97088 m ± 26,08 mm; Y (m) = 5924895,45433 m ± 26,30 mm; Z (m) = -855932,55993 m ± 192,51 mm. It can be seen that the standard deviation resulting from the combined processing of bundle points of Global IGS and regional stations is up to millimeter fraction. Speed position of GNSS CORS GMU1 station in 2012, that is VX = 0,02907 m / year ± 0.25410 m; Vy = 0.13655 m / year ± 0.47235 m; Vz = -0.07798 m / year ± 1.39 m.Keywords: GNSS CORS GMU1 station, GPS connectivity, GAMIT software

Short-Impending Earthquake Anomaly Index Extraction of GNSS Continuous Observation Data in Yunnan, Southwestern China

This paper presents a comprehensive area expansion prediction index method to apply GNSS for short-impending prediction of earthquakes. Based on continuous GNSS observation data from Yunnan Province, a displacement field was detected after data cycle-slip repair using precision data processing software and geophysical field effect model correction. The Yunnan area was divided into 56 grid cells for displacement field interpolation to obtain a more uniform displacement field and a strain field variation time series. The pre-earthquake response of each grid-cell expansion time series was evaluated and synthesized to extract a short-impending earthquake anomaly identification index. The results show that this index indicated occurrence times and hypocenter for earthquakes of magnitude M≥5. Fourteen earthquakes were predicted accurately, and there were five false reports. This index can therefore be used for the short-impending prediction of earthquakes.

Simultaneous estimation of GLONASS pseudorange inter-frequency biases in precise point positioning using undifferenced and uncombined observations

GLONASS precise point positioning (PPP) performance is affected by the inter-frequency biases (IFBs) due to the application of frequency division multiple access technique. In this contribution, the impact of GLONASS pseudorange IFBs on convergence performance and positioning accuracy of GLONASS-only and GPS + GLONASS PPP based on undifferenced and uncombined observation models is investigated. Through a re-parameterization process, the following four pseudorange IFB handling schemes were proposed: neglecting IFBs, modeling IFBs as a linear or quadratic polynomial function of frequency number, and estimating IFBs for each GLONASS satellite. One week of GNSS observation data from 132 International GNSS Service stations was selected to investigate the contribution of simultaneous estimation of GLONASS pseudorange IFBs on GLONASS-only and combined GPS + GLONASS PPP in both static and kinematic modes. The results show that considering IFBs can speed up the convergence of PPP using GLONASS observations by more than 20%. Apart from GLONASS-only kinematic PPP, the positioning accuracy of GLONASS-only and GPS + GLONASS PPP is comparable among the four schemes. Overall, the scheme of estimating IFBs for each GLONASS satellite outperforms the other schemes in both convergence time reduction and positioning accuracy improvement, which indicates that the GLONASS IFBs may not strictly obey a linear or quadratic function relationship with the frequency number.

Ionospheric delay gradient model for GBAS in the Asia-Pacific region

We investigated characteristics of anomalous spatial gradients in ionospheric delay on GNSS signals in the Asia-Pacific (APAC) low-magnetic latitude region in the context of the ground-based augmentation system (GBAS). The ionospheric studies task force established under the Communications, Navigation, and Surveillance subgroup of International Civil Aviation Organization (ICAO) Asia-Pacific Air Navigation Planning and Implementation Regional Group, analyzed GNSS observation data from the Asia-Pacific region to establish a regionally specified ionospheric threat model for GBAS. The largest ionospheric delay gradient value in the analyzed data was 518 mm/km at the L1 frequency (1.57542 GHz), observed at Ishigaki, Japan in April 2008. The upper bound on the ionospheric delay gradient for a common ionospheric threat model for GBAS in the ICAO APAC region was determined to be 600 mm/km, irrespective of satellite elevation angle.